Embedding

The core problem embeddings solve is this: AI systems don't inherently understand that 'customer satisfaction' and 'client happiness' mean similar things. They see them as completely different text strings. Embeddings bridge that gap by converting language into numerical patterns that capture semantic meaning—the actual concept behind the words.

Imagine a library where books aren't organized by title, but by what they're actually about. An embedding does something similar for words and phrases. It places 'Nike' and 'Adidas' close together in numerical space because they're both athletic brands, while placing 'Nike' far from 'accounting software.' This proximity in mathematical space allows AI to recognize relationships humans naturally see.

In marketing tools, embeddings power several critical functions. When you use a semantic search feature in a customer data platform, embeddings are working behind the scenes to find customers whose behavior patterns match your target audience—even if the data is described differently across systems. When an AI chatbot understands that 'How do I track my order?' means the same thing as 'Where's my shipment?', embeddings made that possible. They're also essential for recommendation engines that suggest products based on what customers actually want, not just what they've clicked.

For CMOs evaluating AI tools, embeddings matter because they determine whether your AI system truly understands context or just matches keywords. A tool using embeddings can handle natural language queries, understand customer intent across different phrasing, and make smarter connections in your data. When comparing vendors, ask whether their platform uses embeddings for search and analysis—it's a marker of more sophisticated AI capability. The quality of embeddings also affects accuracy: some vendors use better embedding models than others, which directly impacts whether your AI insights are actually useful or just statistically plausible.